1. Technical Field
The present invention generally relates to medical imaging and, in particular, to a method for automatically segmenting lung nodules using dynamic programming and Expectation Maximization (EM) classification.
2. Background Description
Mortality due to lung cancer is the leading cause for the cancer related deaths in the country. One of the main causes for such a high rate of mortality is the fact that it is very difficult to detect malignant lung nodules. Usually by the time nodules are detected, it is too late, the nodules are too large or too advanced to be effectively cured. Thus, there is a need for lung screening with the motivation for early detection of the malignant lung nodule at a stage where it can be effectively treated. Conventional chest X-rays (CXR) have been utilized for a long time. CXR are described by: Carreira et al., in “Computer-aided Lung Nodule Detection in Chest Radiography”, Lecture notes in Computer Science, Image Analysis Applications and Computer Graphics, 1024, pp. 331-38, 1995; and Braum van Ginneken, in “Computer-aided Diagnosis in Chest Radiography”, PhD Thesis, University of Utrecht, 1970. However, CXR are of limited use as only large lung nodules can be detected using CXR. With the advances in the X-ray Computed Tomography (CT) technology, there is a potential for screening of nodules which can be malignant. Using thin section multi-slice helical CT (hCT) scans, it is now possible to detect nodules which are as small as 3 mm in diameter. The use of hCT scans is described by: Aberle et al., in “Model-based Segmentation Architecture for Lung Nodule Detection in CT”, Radiology, 217(P), 2000; and Aberle et al., in “Computer-aided Method for Lung Micronodule Detection in CT”, Radiology, 217(P), 2000. Usage of a high resolution CT (HRCT) image dataset allows for quantitative measurements, such as, size, shape and density, for each nodule to be made. However, each helical CT scan of a patient leads to a volume consisting of 500 to 600 slices with 512×512 voxels in each slice. Thus, the advantages of having high resolution CT over CXR can be fast lost without the help of efficient image analysis and interpretation methods. Computer-assisted nodule detection has already transformed the way lung cancer screening is done by providing better ways for visualization, detection and characterization of lung nodules. Computer-assisted nodule detection is described by: Aberle et al., in “Computer-aided Method for Lung Micronodule Detection in CT”, Radiology, 217(P), 2000; Kostis et al., in “Computer-aided Diagnosis of Small Pulmonary Nodules”, Seminars in Ultrasound, CT, and MRI, 21(2), pp. 116-28, 2000; Fan et al., in “Automatic Detection of Cellular Necrosis in Epithelial Cell Cultures”, SPIE Medical Imaging, February 2001; and Jacobson et al., in “Evaluation of Segmentation Using Lung Nodule Phantom CT images”, SPIE Medical Imaging, February 2001.
Physical characteristics of the nodules, such as rate of growth, pattern of calcification, type of margins are very important in the investigation of the solitary lung nodules. Every lung nodule grows in volume over time. However, the malignant nodules grow at an exponential rate, which is usually expressed as a tumor's doubling time. Malignant nodules have a doubling time of between 25 to 450 days whereas the benign nodules are stable and have a doubling time of more than 500 days. In addition to the rate of growth of the nodules, the pattern of the calcification is an important indicator of whether the nodule is benign or malignant. Nodules which are centrally or diffuse calcified are usually benign.
Before the nodules can be characterized, it is necessary to detect them in the volume of the 3D CT image dataset that is being acquired. Manual lung nodule detection, which was possible using CXR, is no longer possible. It is necessary to have automated tools that can assist a physician in quickly detecting the nodules. A number of automated lung nodule systems have already been proposed, as described by: Cabello et al., in “Computer-aided Diagnosis: A Neural Network Based Approach to Lung Nodule Detection”, IEEE Transactions on Medical Image, 17(6), pp. 872-80, 1998; Hara et al., in “Nodule Detection on Chest Helical CT Scans by Using a Genetic Algorithm”, Proceedings of the 1997 IASTED International Conference on Intelligent Information, 1997; Aberle et al., in “Computer-aided Method for Lung Micronodule Detection in CT”, Radiology, 217(P), 2000; Fan et al., in “Automatic Detection of Cellular Necrosis in Epithelial Cell Cultures”, SPIE Medical Imaging, February 2001; Cox et al., in “Experiments in Lung Cancer Nodule Detection Using Texture Analysis and Neural Network Classifiers”, at citeseer.nj.nec.com/cox92experiments.html, 1992; Gonzalez et al., in “Application of Computer-performed Holographic Recognition to Lung Nodule Detection and Evaluation in Thoracic CT Scans”, European Congress of Radiology (ECR), 2000; Kanazawa et al., in “Computer-aided Diagnosis of Pulmonary Nodules Based on Helical CT Images, Comput. Med. Imaging Graph., 22, pp. 157-67, 1998; Armato et al., in “Three-dimensional Approach to Lung Nodule Detection in Helical CT”, SPIE Medical Imaging, 3661, pp. 553-59, 1999. While the automated detection of the lung nodules is a very important task, segmenting the nodules once they have been detected remains to be an equally challenging task. The difficulty of the task comes from the fact that some of the nodules may be sitting on the chest wall or on the lung vessels. Accurate and consistent segmentation of the lung nodule over time acquired CT volume datasets is necessary to study the rate of growth of the nodules and hence to predict whether the nodule is malignant or benign.
Accordingly, it would be desirable and highly advantageous to have a method for automatically segmenting lung nodules that can consistently and robustly segment not only solitary nodules but also nodules attached to lung walls and vessels.